In recent years, an image matching technique has been widely used in various fields. According to one exemplified image matching method, a local feature quantity at a feature point in a first image is compared with a local feature quantity at a feature point in a second image to search for the feature point in the second image (hereinafter referred to as “correspondence point”) that corresponds to the feature point in the first image. A set of correspondence points found by search may be statistically processed to recognize the presence of the first image in the second image, and the position of the first image.
The local feature quantity used in the above-mentioned search for the correspondence point may be represented in a binary code. Its typical example is BRIEF (Binary Robust Independent Elementary Features). For each of a plurality of pixel pairs disposed around the feature point, BRIEF is expressed as the local feature quantity calculated based on a luminance difference between pixels. More specifically, a set of bit values (binary code) each corresponding to a sign (positive and negative) of a luminance difference between pixels is calculated as the local feature quantity. According to such method of expressing the local feature quantity as the binary code, the degree of similarity between feature points may be advantageously calculated by high-speed calculation using Hamming distance.
A following image processing technique using the feature quantity has been proposed. For example, a proposed object region extraction apparatus receives a specified region including a predetermined object from an image, extracts a feature quantity of the predetermined object using either a hue component or a brightness component in the specified region, and corrects the position of the object in the specified region using the feature quantity. Further, a proposed authentication system stores each feature point in an image to be authenticated, a difference between color information of each feature point, and a luminance vector of each feature point in association.
For example, Japanese Laid-open Patent Publication Nos. 2011-134117, 2015-149008 and M. Calonder, V. Lepetit, C. Strecha, and P. Fua., “BRIEF: Binary Robust Independent Elementary Features”, In Proceedings of the European Conference on Computer Vision (ECCV), 2010 disclose related arts.